Global anthropogenic CO<sub>2</sub> emissions and uncertainties as a prior for Earth system modelling and data assimilation
<p>The growth in anthropogenic carbon dioxide (<span class="inline-formula">CO<sub>2</sub></span>) emissions acts as a major climate change driver, which has widespread implications across society, influencing the scientific, political, and public sectors. For...
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Acceso en línea: | https://doaj.org/article/d99a6605110541e794e0aaf68d968091 |
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| Sumario: | <p>The growth in anthropogenic carbon dioxide (<span class="inline-formula">CO<sub>2</sub></span>) emissions acts as a major climate change driver, which has widespread implications across
society, influencing the scientific, political, and public sectors. For an increased understanding of the <span class="inline-formula">CO<sub>2</sub></span> emission sources, patterns,
and trends, a link between the emission inventories and observed <span class="inline-formula">CO<sub>2</sub></span> concentrations is best established via Earth system modelling and
data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it
is of utmost importance to know their level of confidence and boundaries well.</p>
<p>Inversions disaggregate the variation in observed atmospheric <span class="inline-formula">CO<sub>2</sub></span> concentration to variability in <span class="inline-formula">CO<sub>2</sub></span> emissions by constraining
the regional distribution of <span class="inline-formula">CO<sub>2</sub></span> fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence
and boundaries for each of these <span class="inline-formula">CO<sub>2</sub></span> fluxes is as important as their intensity, though often not available for bottom-up anthropogenic
<span class="inline-formula">CO<sub>2</sub></span> emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic <span class="inline-formula">CO<sub>2</sub></span> emissions to help assess and
manage the uncertainty in the different emitting sectors. The postprocessor is available under <a href="https://doi.org/10.5281/zenodo.5196190">https://doi.org/10.5281/zenodo.5196190</a> (Choulga et al., 2021). Recommendations are
given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot
spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of
countries based on the quality of their statistical infrastructure. This tool has been applied to the EDGARv4.3.2_FT2015 dataset, resulting in seven input grid maps with upper- and lower-half ranges of uncertainty for the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System. The
dataset is documented and available under <a href="https://doi.org/10.5281/zenodo.3967439">https://doi.org/10.5281/zenodo.3967439</a> (Choulga et al., 2020). While the uncertainty in most emission groups
remains relatively small (5 %–20 %), the largest contribution (usually over 40 %) to the total uncertainty is determined by the OTHER
group (of fuel exploitation and transformation but also agricultural soils and solvents) at the global scale. The uncertainties have been compared for
selected countries to those reported in the inventories submitted to the United Nations Framework Convention on Climate Change and to those assessed
for the European emission grid maps of the Netherlands Organisation for Applied Scientific Research. Several sensitivity experiments are performed
to check (1) the country dependence (by analysing the impact of assuming either a well- or less well-developed statistical infrastructure),
(2) the fuel type dependence (by adding explicit information for each fuel type used per activity from the Intergovernmental Panel on Climate
Change), and (3) the spatial source distribution dependence (by aggregating all emission sources and comparing the effect<span id="page5312"/> against an even
redistribution over the country). The first experiment shows that the SETTLEMENTS group (of energy for buildings) uncertainty changes the most when
development level is changed. The second experiment shows that fuel-specific information reduces uncertainty in emissions only when a country uses
several different fuels in the same amount; when a country mainly uses the most globally typical fuel for an activity, uncertainty values computed with
and without detailed fuel information are the same. The third experiment highlights the importance of spatial mapping.</p> |
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